Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction

TL;DR

This paper compares various machine learning and time series models for predicting household energy consumption, highlighting the most effective approaches like Support Vector Regression, MLP, and Gaussian Processes.

Contribution

It provides a comprehensive comparison of machine learning and time series models for energy forecasting using real datasets and tools like Weka and Python.

Findings

Support Vector Regression outperforms other models

MLP and Gaussian Processes also show strong performance

Weather data impacts forecasting accuracy

Abstract

As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning models for time series forecasting of household energy consumption. Specifically, we use Weka, a data mining tool to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. The models applied are: Multilayer Perceptron, K Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.